File size: 50,387 Bytes
d09f6aa 313f83b 75775c4 100024e 313f83b d6f5eba d09f6aa aee77fd d09f6aa 313f83b d09f6aa 100024e 313f83b d09f6aa 313f83b 100024e 313f83b 100024e 313f83b d09f6aa aee77fd 313f83b 56fd459 75775c4 aee77fd 313f83b 75775c4 a6cf941 d09f6aa a6cf941 d09f6aa a6cf941 d09f6aa 100024e a6cf941 d6f5eba 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e d09f6aa 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e d09f6aa d6f5eba d09f6aa d6f5eba d09f6aa 313f83b d09f6aa 100024e 313f83b 100024e d09f6aa 100024e d09f6aa d6f5eba d09f6aa 313f83b d09f6aa 313f83b d09f6aa 100024e d09f6aa 313f83b d09f6aa 100024e d09f6aa d6f5eba d09f6aa 313f83b 75775c4 d09f6aa d6f5eba 100024e d09f6aa 100024e d09f6aa 100024e d09f6aa 100024e d09f6aa d6f5eba 313f83b 100024e d09f6aa 100024e d09f6aa 100024e d09f6aa 100024e d09f6aa d6f5eba a6cf941 100024e 313f83b 100024e 313f83b 100024e d09f6aa 100024e d09f6aa d6f5eba a6cf941 d09f6aa 100024e d09f6aa 100024e d6f5eba 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e d09f6aa 100024e d09f6aa 100024e d09f6aa 313f83b d09f6aa 313f83b d09f6aa a6cf941 75775c4 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e d09f6aa 100024e d09f6aa 100024e d09f6aa aee77fd 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e 313f83b 100024e d09f6aa 75775c4 100024e d09f6aa 75775c4 aee77fd d09f6aa aee77fd d09f6aa 876ef0e d09f6aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 |
# Standard library imports
import asyncio
import os
import re
from datetime import datetime
import gradio as gr
import pandas as pd
from ankigen_core.card_generator import (
AVAILABLE_MODELS,
orchestrate_card_generation,
) # GENERATION_MODES is internal to card_generator
from ankigen_core.exporters import (
export_dataframe_to_apkg,
export_dataframe_to_csv,
) # Anki models (BASIC_MODEL, CLOZE_MODEL) are internal to exporters
from ankigen_core.learning_path import analyze_learning_path
from ankigen_core.llm_interface import (
OpenAIClientManager,
) # structured_output_completion is internal to core modules
from ankigen_core.ui_logic import (
crawl_and_generate,
create_crawler_main_mode_elements,
update_mode_visibility,
use_selected_subjects,
)
from ankigen_core.utils import (
ResponseCache,
get_logger,
) # fetch_webpage_text is used by card_generator
# --- Initialization ---
logger = get_logger()
response_cache = ResponseCache() # Initialize cache
client_manager = OpenAIClientManager() # Initialize client manager
# Agent system is required
AGENTS_AVAILABLE_APP = True
logger.info("Agent system is available")
js_storage = """
async () => {
const loadDecks = () => {
const decks = localStorage.getItem('ankigen_decks');
return decks ? JSON.parse(decks) : [];
};
const saveDecks = (decks) => {
localStorage.setItem('ankigen_decks', JSON.stringify(decks));
};
window.loadStoredDecks = loadDecks;
window.saveStoredDecks = saveDecks;
return loadDecks();
}
"""
try:
custom_theme = gr.themes.Soft().set( # type: ignore
body_background_fill="*background_fill_secondary",
block_background_fill="*background_fill_primary",
block_border_width="0",
button_primary_background_fill="*primary_500",
button_primary_text_color="white",
)
except (AttributeError, ImportError):
# Fallback for older gradio versions or when themes are not available
custom_theme = None
# --- Example Data for Initialization ---
example_data = pd.DataFrame(
[
[
"1.1",
"SQL Basics",
"basic",
"What is a SELECT statement used for?",
"Retrieving data from one or more database tables.",
"The SELECT statement is the most common command in SQL...",
"```sql\nSELECT column1, column2 FROM my_table WHERE condition;\n```",
["Understanding of database tables"],
["Retrieve specific data"],
["❌ SELECT * is always efficient (Reality: Can be slow for large tables)"],
"beginner",
],
[
"2.1",
"Python Fundamentals",
"cloze",
"The primary keyword to define a function in Python is {{c1::def}}.",
"def",
"Functions are defined using the `def` keyword...",
"""```python
def greet(name):
print(f"Hello, {name}!")
```""",
["Basic programming concepts"],
["Define reusable blocks of code"],
["❌ Forgetting the colon (:) after the definition"],
"beginner",
],
],
columns=[
"Index",
"Topic",
"Card_Type",
"Question",
"Answer",
"Explanation",
"Example",
"Prerequisites",
"Learning_Outcomes",
"Common_Misconceptions",
"Difficulty",
],
)
# -------------------------------------
# --- Helper function for log viewing (Subtask 15.5) ---
def get_recent_logs(logger_name="ankigen") -> str:
"""Fetches the most recent log entries from the current day's log file."""
try:
log_dir = os.path.join(os.path.expanduser("~"), ".ankigen", "logs")
timestamp = datetime.now().strftime("%Y%m%d")
# Use the logger_name parameter to construct the log file name
log_file = os.path.join(log_dir, f"{logger_name}_{timestamp}.log")
if os.path.exists(log_file):
with open(log_file) as f:
lines = f.readlines()
# Display last N lines, e.g., 100
return "\n".join(lines[-100:]) # Ensured this is standard newline
return f"Log file for today ({log_file}) not found or is empty."
except Exception as e:
# Use the main app logger to log this error, but don't let it crash the UI
# function
logger.error(f"Error reading logs: {e}", exc_info=True)
return f"Error reading logs: {e!s}"
def create_ankigen_interface():
logger.info("Creating AnkiGen Gradio interface...")
with gr.Blocks(
theme=custom_theme,
title="AnkiGen",
css="""
#footer {display:none !important}
.tall-dataframe {min-height: 500px !important}
.contain {max-width: 100% !important; margin: auto;}
.output-cards {border-radius: 8px; box-shadow: 0 4px 6px -1px rgba(0,0,0,0.1);}
.hint-text {font-size: 0.9em; color: #666; margin-top: 4px;}
.export-group > .gradio-group { margin-bottom: 0 !important; padding-bottom: 5px !important; }
/* REMOVING CSS previously intended for DataFrame readability to ensure plain text */
/*
.explanation-text {
background: #f0fdf4;
border-left: 3px solid #4ade80;
padding: 0.5em;
margin-bottom: 0.5em;
border-radius: 4px;
}
.example-text-plain {
background: #fff7ed;
border-left: 3px solid #f97316;
padding: 0.5em;
margin-bottom: 0.5em;
border-radius: 4px;
}
pre code {
display: block;
padding: 0.8em;
background: #1e293b;
color: #e2e8f0;
border-radius: 4px;
overflow-x: auto;
font-family: 'Fira Code', 'Consolas', monospace;
font-size: 0.9em;
margin-bottom: 0.5em;
}
*/
""",
js=js_storage,
) as ankigen:
with gr.Column(elem_classes="contain"):
gr.Markdown("# 📚 AnkiGen - Advanced Anki Card Generator")
gr.Markdown("#### Generate comprehensive Anki flashcards using AI.")
with gr.Accordion("Configuration Settings", open=True):
with gr.Row():
with gr.Column(scale=1):
generation_mode = gr.Radio(
choices=[
("Single Subject", "subject"),
("Learning Path", "path"),
("From Text", "text"),
("From Web", "web"),
],
value="subject",
label="Generation Mode",
info="Choose how you want to generate content",
)
with gr.Group() as subject_mode:
subject = gr.Textbox(
label="Subject",
placeholder="e.g., 'Basic SQL Concepts'",
)
with gr.Group(visible=False) as path_mode:
description = gr.Textbox(
label="Learning Goal",
placeholder="Paste a job description...",
lines=5,
)
analyze_button = gr.Button(
"Analyze & Break Down",
variant="secondary",
)
with gr.Group(visible=False) as text_mode:
source_text = gr.Textbox(
label="Source Text",
placeholder="Paste text here...",
lines=15,
)
with gr.Group(visible=False) as web_mode:
# --- BEGIN INTEGRATED CRAWLER UI (Task 16) ---
logger.info(
"Setting up integrated Web Crawler UI elements...",
)
(
crawler_input_ui_elements, # List of inputs like URL, depth, model, patterns
web_crawl_button, # Specific button to trigger crawl
web_crawl_progress_bar,
web_crawl_status_textbox,
web_crawl_custom_system_prompt,
web_crawl_custom_user_prompt_template,
web_crawl_use_sitemap_checkbox,
web_crawl_sitemap_url_textbox,
) = create_crawler_main_mode_elements()
# Unpack crawler_input_ui_elements for clarity and use
web_crawl_url_input = crawler_input_ui_elements[0]
web_crawl_max_depth_slider = crawler_input_ui_elements[1]
web_crawl_req_per_sec_slider = crawler_input_ui_elements[2]
web_crawl_model_dropdown = crawler_input_ui_elements[3]
web_crawl_include_patterns_textbox = (
crawler_input_ui_elements[4]
)
web_crawl_exclude_patterns_textbox = (
crawler_input_ui_elements[5]
)
# --- END INTEGRATED CRAWLER UI ---
api_key_input = gr.Textbox(
label="OpenAI API Key",
type="password",
placeholder="Enter your OpenAI API key (sk-...)",
value=os.getenv("OPENAI_API_KEY", ""),
info="Your key is used solely for processing your requests.",
elem_id="api-key-textbox",
)
with gr.Column(scale=1):
with gr.Accordion("Advanced Settings", open=False):
model_choices_ui = [
(m["label"], m["value"]) for m in AVAILABLE_MODELS
]
default_model_value = next(
(
m["value"]
for m in AVAILABLE_MODELS
if "nano" in m["value"].lower()
),
AVAILABLE_MODELS[0]["value"],
)
model_choice = gr.Dropdown(
choices=model_choices_ui,
value=default_model_value,
label="Model Selection",
info="Select AI model for generation",
allow_custom_value=True,
)
_model_info = gr.Markdown(
"**gpt-4.1**: Best quality | **gpt-4.1-nano**: Faster/Cheaper",
)
topic_number = gr.Slider(
label="Number of Topics",
minimum=2,
maximum=20,
step=1,
value=2,
)
cards_per_topic = gr.Slider(
label="Cards per Topic",
minimum=2,
maximum=30,
step=1,
value=3,
)
preference_prompt = gr.Textbox(
label="Learning Preferences",
placeholder="e.g., 'Beginner focus'",
lines=3,
)
generate_cloze_checkbox = gr.Checkbox(
label="Generate Cloze Cards (Experimental)",
value=False,
)
# Agent System Controls (simplified since we're agent-only)
if AGENTS_AVAILABLE_APP:
# Hidden dropdown for compatibility - always set to agent_only
agent_mode_dropdown = gr.Dropdown(
choices=[("Agent Only", "agent_only")],
value="agent_only",
label="Agent Mode",
visible=False,
)
with gr.Accordion("Agent Configuration", open=False):
gr.Markdown("**Core Generation Pipeline**")
enable_subject_expert = gr.Checkbox(
label="Subject Expert Agent",
value=True,
info="Domain-specific expertise",
)
enable_generation_coordinator = gr.Checkbox(
label="Generation Coordinator",
value=True,
info="Orchestrates multi-agent generation",
)
gr.Markdown("**Quality Assurance**")
enable_content_judge = gr.Checkbox(
label="Content Accuracy Judge",
value=True,
info="Factual correctness validation",
)
enable_clarity_judge = gr.Checkbox(
label="Clarity Judge",
value=True,
info="Language clarity and comprehension",
)
gr.Markdown("**Optional Enhancements**")
enable_pedagogical_agent = gr.Checkbox(
label="Pedagogical Agent",
value=False,
info="Educational effectiveness review",
)
enable_pedagogical_judge = gr.Checkbox(
label="Pedagogical Judge",
value=False,
info="Learning theory compliance",
)
enable_enhancement_agent = gr.Checkbox(
label="Enhancement Agent",
value=False,
info="Content enrichment and metadata",
)
with gr.Accordion(
"🛠️ Agent Model Selection", open=False
):
gr.Markdown("**Individual Agent Models**")
# Generator models
subject_expert_model = gr.Dropdown(
choices=model_choices_ui,
value="gpt-4.1",
label="Subject Expert Model",
info="Model for domain expertise",
allow_custom_value=True,
)
generation_coordinator_model = gr.Dropdown(
choices=model_choices_ui,
value="gpt-4.1-nano",
label="Generation Coordinator Model",
info="Model for orchestration",
allow_custom_value=True,
)
# Judge models
content_judge_model = gr.Dropdown(
choices=model_choices_ui,
value="gpt-4.1",
label="Content Accuracy Judge Model",
info="Model for fact-checking",
allow_custom_value=True,
)
clarity_judge_model = gr.Dropdown(
choices=model_choices_ui,
value="gpt-4.1-nano",
label="Clarity Judge Model",
info="Model for language clarity",
allow_custom_value=True,
)
# Enhancement models
pedagogical_agent_model = gr.Dropdown(
choices=model_choices_ui,
value="gpt-4.1",
label="Pedagogical Agent Model",
info="Model for educational theory",
allow_custom_value=True,
)
enhancement_agent_model = gr.Dropdown(
choices=model_choices_ui,
value="gpt-4.1",
label="Enhancement Agent Model",
info="Model for content enrichment",
allow_custom_value=True,
)
else:
# Placeholder when agents not available
agent_mode_dropdown = gr.Dropdown(
choices=[("Legacy Only", "legacy")],
value="legacy",
label="Agent Mode",
info="Agent system not available",
interactive=False,
)
enable_subject_expert = gr.Checkbox(
value=False, visible=False
)
enable_generation_coordinator = gr.Checkbox(
value=False, visible=False
)
enable_pedagogical_agent = gr.Checkbox(
value=False, visible=False
)
enable_content_judge = gr.Checkbox(
value=False, visible=False
)
enable_clarity_judge = gr.Checkbox(
value=False, visible=False
)
enable_pedagogical_judge = gr.Checkbox(
value=False, visible=False
)
enable_enhancement_agent = gr.Checkbox(
value=False, visible=False
)
# Hidden model dropdowns for non-agent mode
subject_expert_model = gr.Dropdown(
value="gpt-4.1", visible=False
)
generation_coordinator_model = gr.Dropdown(
value="gpt-4.1-nano", visible=False
)
content_judge_model = gr.Dropdown(
value="gpt-4.1", visible=False
)
clarity_judge_model = gr.Dropdown(
value="gpt-4.1-nano", visible=False
)
pedagogical_agent_model = gr.Dropdown(
value="gpt-4.1", visible=False
)
enhancement_agent_model = gr.Dropdown(
value="gpt-4.1", visible=False
)
generate_button = gr.Button("Generate Cards", variant="primary")
with gr.Group(visible=False) as path_results:
gr.Markdown("### Learning Path Analysis")
subjects_list = gr.Dataframe(
headers=["Subject", "Prerequisites", "Time Estimate"],
label="Recommended Subjects",
interactive=False,
)
learning_order = gr.Markdown("### Recommended Learning Order")
projects = gr.Markdown("### Suggested Projects")
use_subjects = gr.Button("Use These Subjects ℹ️", variant="primary")
gr.Markdown(
"*Click to copy subjects to main input*",
elem_classes="hint-text",
)
with gr.Group() as cards_output:
gr.Markdown("### Generated Cards")
with gr.Accordion("Output Format", open=False):
gr.Markdown(
"Cards: Index, Topic, Type, Q, A, Explanation, Example, Prerequisites, Outcomes, Misconceptions, Difficulty. Export: CSV, .apkg",
)
with gr.Accordion("Example Card Format", open=False):
gr.Code(
label="Example Card",
value='{"front": ..., "back": ..., "metadata": ...}',
language="json",
)
output = gr.DataFrame(
value=example_data,
headers=[
"Index",
"Topic",
"Card_Type",
"Question",
"Answer",
"Explanation",
"Example",
"Prerequisites",
"Learning_Outcomes",
"Common_Misconceptions",
"Difficulty",
],
datatype=[
"number",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
],
interactive=True,
elem_classes="tall-dataframe",
wrap=True,
column_widths=[
50,
100,
80,
200,
200,
250,
200,
150,
150,
150,
100,
],
)
total_cards_html = gr.HTML(
value="<div><b>Total Cards Generated:</b> <span id='total-cards-count'>0</span></div>",
visible=False,
)
# Token usage display
token_usage_html = gr.HTML(
value="<div style='margin-top: 8px;'><b>Token Usage:</b> <span id='token-usage-display'>No usage data</span></div>",
visible=True,
)
# Export buttons
with gr.Row(elem_classes="export-group"):
export_csv_button = gr.Button("Export to CSV")
export_apkg_button = gr.Button("Export to .apkg")
download_file_output = gr.File(label="Download Deck", visible=False)
# --- Event Handlers --- (Updated to use functions from ankigen_core)
generation_mode.change(
fn=update_mode_visibility,
inputs=[
generation_mode,
subject,
description,
source_text,
web_crawl_url_input,
],
outputs=[
subject_mode,
path_mode,
text_mode,
web_mode,
path_results,
cards_output,
subject,
description,
source_text,
web_crawl_url_input,
output,
subjects_list,
learning_order,
projects,
total_cards_html,
],
)
# Define an async wrapper for the analyze_learning_path partial
async def handle_analyze_click(
api_key_val,
description_val,
model_choice_val,
progress=gr.Progress(track_tqdm=True), # Added progress tracker
):
try:
# Call analyze_learning_path directly, as client_manager and response_cache are in scope
return await analyze_learning_path(
client_manager, # from global scope
response_cache, # from global scope
api_key_val,
description_val,
model_choice_val,
)
except gr.Error as e: # Catch the specific Gradio error
logger.error(f"Learning path analysis failed: {e}", exc_info=True)
# Re-raise the error so Gradio displays it to the user
# And return appropriate empty updates for the outputs
# to prevent a subsequent Gradio error about mismatched return values.
gr.Error(str(e)) # This will be shown in the UI.
empty_subjects_df = pd.DataFrame(
columns=["Subject", "Prerequisites", "Time Estimate"],
)
return (
gr.update(
value=empty_subjects_df,
), # For subjects_list (DataFrame)
gr.update(value=""), # For learning_order (Markdown)
gr.update(value=""), # For projects (Markdown)
)
analyze_button.click(
fn=handle_analyze_click, # MODIFIED: Use the new async handler
inputs=[
api_key_input,
description,
model_choice,
],
outputs=[subjects_list, learning_order, projects],
)
use_subjects.click(
fn=use_selected_subjects,
inputs=[subjects_list],
outputs=[
generation_mode,
subject_mode,
path_mode,
text_mode,
web_mode,
path_results,
cards_output,
subject,
description,
source_text,
web_crawl_url_input,
topic_number,
preference_prompt,
output,
subjects_list,
learning_order,
projects,
total_cards_html,
],
)
# Define an async wrapper for the orchestrate_card_generation partial
async def handle_generate_click(
api_key_input_val,
subject_val,
generation_mode_val,
source_text_val,
url_input_val,
model_choice_val,
topic_number_val,
cards_per_topic_val,
preference_prompt_val,
generate_cloze_checkbox_val,
agent_mode_val,
enable_subject_expert_val,
enable_generation_coordinator_val,
enable_pedagogical_agent_val,
enable_content_judge_val,
enable_clarity_judge_val,
enable_pedagogical_judge_val,
enable_enhancement_agent_val,
subject_expert_model_val,
generation_coordinator_model_val,
content_judge_model_val,
clarity_judge_model_val,
pedagogical_agent_model_val,
enhancement_agent_model_val,
progress=gr.Progress(track_tqdm=True), # Added progress tracker
):
# Apply agent settings if agents are available
if AGENTS_AVAILABLE_APP:
import os
# Set agent mode
os.environ["ANKIGEN_AGENT_MODE"] = agent_mode_val
# Set individual agent flags (using correct environment variable names)
os.environ["ANKIGEN_ENABLE_SUBJECT_EXPERT"] = str(
enable_subject_expert_val
).lower()
os.environ["ANKIGEN_ENABLE_GENERATION_COORDINATOR"] = str(
enable_generation_coordinator_val
).lower()
os.environ["ANKIGEN_ENABLE_PEDAGOGICAL_AGENT"] = str(
enable_pedagogical_agent_val
).lower()
os.environ["ANKIGEN_ENABLE_CONTENT_JUDGE"] = str(
enable_content_judge_val
).lower()
os.environ["ANKIGEN_ENABLE_CLARITY_JUDGE"] = str(
enable_clarity_judge_val
).lower()
os.environ["ANKIGEN_ENABLE_PEDAGOGICAL_JUDGE"] = str(
enable_pedagogical_judge_val
).lower()
os.environ["ANKIGEN_ENABLE_ENHANCEMENT_AGENT"] = str(
enable_enhancement_agent_val
).lower()
# Enable additional required flags for proper agent coordination
os.environ["ANKIGEN_ENABLE_JUDGE_COORDINATOR"] = (
"true" # Required for judge coordination
)
os.environ["ANKIGEN_ENABLE_PARALLEL_JUDGING"] = (
"true" # Enable parallel judging for performance
)
# Configure agent models from UI selections
model_overrides = {
"subject_expert": subject_expert_model_val,
"generation_coordinator": generation_coordinator_model_val,
"content_accuracy_judge": content_judge_model_val,
"clarity_judge": clarity_judge_model_val,
"pedagogical_agent": pedagogical_agent_model_val,
"enhancement_agent": enhancement_agent_model_val,
}
# Template variables for Jinja rendering
template_vars = {
"subject": subject_val or "general studies",
"difficulty": "intermediate", # Could be made configurable
"topic": subject_val or "general concepts",
}
# Initialize config manager with model overrides and template variables
from ankigen_core.agents.config import get_config_manager
get_config_manager(model_overrides, template_vars)
# Log the agent configuration
logger.info(f"Agent mode set to: {agent_mode_val}")
logger.info(f"Model overrides: {model_overrides}")
logger.info(
f"Active agents: Subject Expert={enable_subject_expert_val}, Generation Coordinator={enable_generation_coordinator_val}, Content Judge={enable_content_judge_val}, Clarity Judge={enable_clarity_judge_val}"
)
# Reload feature flags to pick up the new environment variables
try:
# Agent system is available
logger.info("Agent system enabled")
except Exception as e:
logger.warning(f"Failed to reload feature flags: {e}")
# Recreate the partial function call, but now it can be awaited
# The actual orchestrate_card_generation is already partially applied with client_manager and response_cache
# So, we need to get that specific partial object if it's stored, or redefine the partial logic here.
# For simplicity and clarity, let's assume direct call to orchestrate_card_generation directly here
return await orchestrate_card_generation(
client_manager, # from global scope
response_cache, # from global scope
api_key_input_val,
subject_val,
generation_mode_val,
source_text_val,
url_input_val,
model_choice_val,
topic_number_val,
cards_per_topic_val,
preference_prompt_val,
generate_cloze_checkbox_val,
)
# Expect 3-tuple return (dataframe, total_cards_html, token_usage_html)
generate_button.click(
fn=handle_generate_click, # MODIFIED: Use the new async handler
inputs=[
api_key_input,
subject,
generation_mode,
source_text,
web_crawl_url_input,
model_choice,
topic_number,
cards_per_topic,
preference_prompt,
generate_cloze_checkbox,
agent_mode_dropdown,
enable_subject_expert,
enable_generation_coordinator,
enable_pedagogical_agent,
enable_content_judge,
enable_clarity_judge,
enable_pedagogical_judge,
enable_enhancement_agent,
subject_expert_model,
generation_coordinator_model,
content_judge_model,
clarity_judge_model,
pedagogical_agent_model,
enhancement_agent_model,
],
outputs=[output, total_cards_html, token_usage_html],
show_progress="full",
)
# Define handler for CSV export (similar to APKG)
async def handle_export_dataframe_to_csv_click(df: pd.DataFrame):
if df is None or df.empty:
gr.Warning("No cards generated to export to CSV.")
return gr.update(value=None, visible=False)
try:
# export_dataframe_to_csv from exporters.py returns a relative path
# or a filename if no path was part of its input.
# It already handles None input for filename_suggestion.
exported_path_relative = await asyncio.to_thread(
export_dataframe_to_csv,
df,
filename_suggestion="ankigen_cards.csv",
)
if exported_path_relative:
exported_path_absolute = os.path.abspath(exported_path_relative)
gr.Info(
f"CSV ready for download: {os.path.basename(exported_path_absolute)}",
)
return gr.update(value=exported_path_absolute, visible=True)
# This case might happen if export_dataframe_to_csv itself had an internal issue
# and returned None, though it typically raises an error or returns path.
gr.Warning("CSV export failed or returned no path.")
return gr.update(value=None, visible=False)
except Exception as e:
logger.error(
f"Error exporting DataFrame to CSV: {e}",
exc_info=True,
)
gr.Error(f"Failed to export to CSV: {e!s}")
return gr.update(value=None, visible=False)
export_csv_button.click(
fn=handle_export_dataframe_to_csv_click, # Use the new handler
inputs=[output],
outputs=[download_file_output],
api_name="export_main_to_csv",
)
# Define handler for APKG export from DataFrame (Item 5)
async def handle_export_dataframe_to_apkg_click(
df: pd.DataFrame,
subject_for_deck_name: str,
):
if df is None or df.empty:
gr.Warning("No cards generated to export.")
return gr.update(value=None, visible=False)
timestamp_for_name = datetime.now().strftime("%Y%m%d_%H%M%S")
deck_name_inside_anki = (
"AnkiGen Exported Deck" # Default name inside Anki
)
if subject_for_deck_name and subject_for_deck_name.strip():
clean_subject = re.sub(
r"[^a-zA-Z0-9\s_.-]",
"",
subject_for_deck_name.strip(),
)
deck_name_inside_anki = f"AnkiGen - {clean_subject}"
elif not df.empty and "Topic" in df.columns and df["Topic"].iloc[0]:
first_topic = df["Topic"].iloc[0]
clean_first_topic = re.sub(
r"[^a-zA-Z0-9\s_.-]",
"",
str(first_topic).strip(),
)
deck_name_inside_anki = f"AnkiGen - {clean_first_topic}"
else:
deck_name_inside_anki = f"AnkiGen Deck - {timestamp_for_name}" # Fallback with timestamp
# Construct the output filename and path
# Use the deck_name_inside_anki for the base of the filename for consistency
base_filename = re.sub(r"[^a-zA-Z0-9_.-]", "_", deck_name_inside_anki)
output_filename = f"{base_filename}_{timestamp_for_name}.apkg"
output_dir = "output_decks" # As defined in export_dataframe_to_apkg
os.makedirs(output_dir, exist_ok=True) # Ensure directory exists
full_output_path = os.path.join(output_dir, output_filename)
try:
# Call export_dataframe_to_apkg with correct arguments:
# 1. df (DataFrame)
# 2. output_path (full path for the .apkg file)
# 3. deck_name (name of the deck inside Anki)
exported_path_relative = await asyncio.to_thread(
export_dataframe_to_apkg,
df,
full_output_path, # Pass the constructed full output path
deck_name_inside_anki, # This is the name for the deck inside the .apkg file
)
# export_dataframe_to_apkg returns the actual path it used, which should match full_output_path
exported_path_absolute = os.path.abspath(exported_path_relative)
gr.Info(
f"Successfully exported deck '{deck_name_inside_anki}' to {exported_path_absolute}",
)
return gr.update(value=exported_path_absolute, visible=True)
except Exception as e:
logger.error(
f"Error exporting DataFrame to APKG: {e}",
exc_info=True,
)
gr.Error(f"Failed to export to APKG: {e!s}")
return gr.update(value=None, visible=False)
# Wire button to handler (Item 6)
export_apkg_button.click(
fn=handle_export_dataframe_to_apkg_click,
inputs=[output, subject], # Added subject as input
outputs=[download_file_output],
api_name="export_main_to_apkg",
)
async def handle_web_crawl_click(
api_key_val: str,
url: str,
max_depth: int,
req_per_sec: float,
model: str, # This is the model for LLM processing of crawled content
include_patterns: str,
exclude_patterns: str,
custom_system_prompt: str,
custom_user_prompt_template: str,
use_sitemap: bool,
sitemap_url: str,
progress=gr.Progress(track_tqdm=True),
):
progress(0, desc="Initializing web crawl...")
yield {
web_crawl_status_textbox: gr.update(
value="Initializing web crawl...",
),
output: gr.update(value=None), # Clear main output table
total_cards_html: gr.update(
visible=False,
value="<div><b>Total Cards Generated:</b> <span id='total-cards-count'>0</span></div>",
),
}
if not api_key_val:
logger.error("API Key is missing for web crawler operation.")
yield {
web_crawl_status_textbox: gr.update(
value="Error: OpenAI API Key is required.",
),
}
return
try:
await client_manager.initialize_client(api_key_val)
except Exception as e:
logger.error(
f"Failed to initialize OpenAI client for crawler: {e}",
exc_info=True,
)
yield {
web_crawl_status_textbox: gr.update(
value=f"Error: Client init failed: {e!s}",
),
}
return
message, cards_list_of_dicts, _ = await crawl_and_generate(
url=url,
max_depth=max_depth,
crawler_requests_per_second=req_per_sec,
include_patterns=include_patterns,
exclude_patterns=exclude_patterns,
model=model,
export_format_ui="", # No longer used for direct export from crawl_and_generate
custom_system_prompt=custom_system_prompt,
custom_user_prompt_template=custom_user_prompt_template,
use_sitemap=use_sitemap,
sitemap_url_str=sitemap_url,
client_manager=client_manager, # Passed from global scope
progress=progress, # Gradio progress object
status_textbox=web_crawl_status_textbox, # Specific status textbox for crawl
)
if cards_list_of_dicts:
try:
# Convert List[Dict] to Pandas DataFrame for the main output component
preview_df_value = pd.DataFrame(cards_list_of_dicts)
# Ensure columns match the main output dataframe
# The `generate_cards_from_crawled_content` which produces `cards_list_of_dicts`
# should already format it correctly. If not, mapping is needed here.
# For now, assume it matches the main table structure expected by `gr.Dataframe(value=example_data)`
# Check if columns match example_data, if not, reorder/rename or log warning
if not preview_df_value.empty:
expected_cols = example_data.columns.tolist()
# Basic check, might need more robust mapping if structures differ significantly
if not all(
col in preview_df_value.columns for col in expected_cols
):
logger.warning(
"Crawled card data columns mismatch main output, attempting to use available data.",
)
# Potentially select only common columns or reindex if necessary
# For now, we'll pass it as is, Gradio might handle extra/missing cols gracefully or error.
num_cards = len(preview_df_value)
total_cards_update = f"<div><b>Total Cards Prepared from Crawl:</b> <span id='total-cards-count'>{num_cards}</span></div>"
yield {
web_crawl_status_textbox: gr.update(value=message),
output: gr.update(value=preview_df_value),
total_cards_html: gr.update(
visible=True,
value=total_cards_update,
),
}
except Exception as e:
logger.error(
f"Error converting crawled cards to DataFrame: {e}",
exc_info=True,
)
yield {
web_crawl_status_textbox: gr.update(
value=f"{message} (Error displaying cards: {e!s})",
),
output: gr.update(value=None),
total_cards_html: gr.update(visible=False),
}
else:
yield {
web_crawl_status_textbox: gr.update(
value=message,
), # Message from crawl_and_generate (e.g. no cards)
output: gr.update(value=None),
total_cards_html: gr.update(visible=False),
}
web_crawl_button.click(
fn=handle_web_crawl_click,
inputs=[
api_key_input,
web_crawl_url_input,
web_crawl_max_depth_slider,
web_crawl_req_per_sec_slider,
web_crawl_model_dropdown, # Model for LLM processing of content
web_crawl_include_patterns_textbox,
web_crawl_exclude_patterns_textbox,
web_crawl_custom_system_prompt,
web_crawl_custom_user_prompt_template,
web_crawl_use_sitemap_checkbox,
web_crawl_sitemap_url_textbox,
],
outputs=[
web_crawl_status_textbox, # Specific status for crawl
output, # Main output DataFrame
total_cards_html, # Main total cards display
],
# Removed progress_bar from outputs as it's handled by gr.Progress(track_tqdm=True)
)
logger.info("AnkiGen Gradio interface creation complete.")
return ankigen
# --- Main Execution --- (Runs if script is executed directly)
if __name__ == "__main__":
try:
ankigen_interface = create_ankigen_interface()
logger.info("Launching AnkiGen Gradio interface...")
ankigen_interface.launch()
except Exception as e:
logger.critical(f"Failed to launch Gradio interface: {e}", exc_info=True)
|